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Machine Learning Based County Level Phenotypes Related to Diabetes Prevalence
Md Fitrat Hossain1, Fadia T Shaya1
1University of Maryland School of Pharmacy.
Summary
Diabetes prevalence is rising in the US, linked to social determinants of health (SDoH). This study identified high food insecurity and poverty as key factors associated with increased diabetes rates, informing targeted interventions.
Area of Science:
- Public Health
- Epidemiology
- Health Disparities
Background:
- Rising diabetes prevalence in the US necessitates understanding contributing factors.
- Limited research has explored the association between social determinants of health (SDoH) and diabetes rates.
- County-level SDoH data can reveal geographic patterns in diabetes prevalence.
Purpose of the Study:
- To develop county-level phenotypes based on SDoH associated with diabetes prevalence.
- To identify specific SDoH that are significant risk factors for higher diabetes rates.
- To inform tailored, region-based interventions for diabetes prevention and management.
Main Methods:
- Utilized machine learning algorithms, including Classification and Regression Tree (CART) models, to define SDoH-based phenotypes.
- Employed Random Forest analysis to identify additional risk factors for diabetes prevalence.
- Categorized US counties into five distinct groups based on identified SDoH phenotypes.
Main Results:
- The CART model identified five distinct county phenotypes related to diabetes prevalence.
- Counties characterized by high food insecurity (over 16%) and high poverty (over 24%) exhibited elevated mean diabetes prevalence rates (17.64%, SD 2.42).
- Identified specific SDoH combinations linked to increased diabetes risk.
Conclusions:
- SDoH, particularly food insecurity and poverty, are significantly associated with higher diabetes prevalence at the county level.
- The developed phenotypes provide a framework for understanding geographic variations in diabetes.
- Findings support the development of targeted, region-specific public health and policy interventions to mitigate diabetes prevalence and improve health outcomes.
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